Highlights
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Patient registries are data systems organized to allows the prospective collection of clinical data to assess specific outcomes.
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Types of registries include administrative, linked, and disease-, procedure- or pathology-, or product-specific registries.
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Registry studies are typically considered level II or III evidence, however the advent of registry based RCTs may change this paradigm.
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Strengths of registries include the volume of data available, diversity of included participants, and efficient enrollment and data collection.
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Limitations of registries include variable quality of data, lack of active follow-up, and, often, a lack of detail in the data collected.
Abstract
Patient registries have grown in size and number along with general computing power
and digitization of the healthcare world. In contrast to databases, registries are
typically patient data systematically created and collected for the express purpose
of answering health-related questions. Registries can be disease-, procedure-, pathology-,
or product-based in nature. Registry-based studies typically fit into Level II or
III in the hierarchy of evidence-based medicine. However, a recent advent in the use
of registry data has been the development and execution of registry-based trials,
such as the TASTE trial, which may elevate registry-based studies into the realm of
Level I evidence. Some strengths of registries include the sheer volume of data, the
inclusion of a diverse set of participants, and their ability to be linked to other
registries and databases. Limitations of registries include variable quality of the
collected data, and a lack of active follow-up (which may underestimate rates of adverse
events). As with any study type, the intended design does not automatically lead to
a study of a certain quality. While no specific tool exists for assessing the quality
of a registry-based study, some important considerations include ensuring the registry
is appropriate for the question being asked, whether the patient population is representative,
the presence of an appropriate comparison group, and the validity and generalizability
of the registry in question. The future of clinical registries remains to be seen,
but the incorporation of big data and machine learning algorithms will certainly play
an important role.
Keywords
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Article info
Publication history
Published online: December 12, 2021
Accepted:
December 4,
2021
Publication stage
In Press Journal Pre-ProofIdentification
Copyright
© 2021 Elsevier Ltd. All rights reserved.